V

var

var computes the variance of the elements of an array regarding a given dimension.

VaRauxdiagcat

subroutine for VaRdiagtable.

VaRauxsums

subroutine for VaRpred (with option sums), calculates the transformation matrix.

VaRcdfDG

approximates the cumulative distribution function (CDF) for the class of quadratic forms of Gaussian vectors.

VaRcgfDG

computes the cumulant generating function (cgf) for the class of quadratic forms of Gaussian vectors.

VaRcharfDG

computes the characteristic function for the class of quadratic forms of Gaussian vectors.

VaRcharfDGF2

computes the Fourier transform of an approximating Gaussian cumulative distribution function (CDF) for the class of quadratic forms of Gaussian vectors.

varcl

Computes the variance of elements of a given interval bounds matrix (classified data)

VaRcopula

calculates the copula function, its derivatives and the inverse (in two dimensions).

VaRcorrfDGF2

computes the cumulative distribution function (CDF) of an approximated normal distribution for the class of quadratic forms of Gaussian vectors.

VaRcredN

Simulates a default distribution for a portfolio of homogeneous obligors where the default driver is normally distributed. Returns mean, variance and the quantile chosen.

VaRcredN2

Simulates a default distribution for a portfolio of obligors where the (joint) default driver is normally distributed. The dependence structure imposed corresponds to two homogeneous subportfolios driven by two default factors. Returns mean, variance and the quantile chosen.

VaRcredTcop

Simulates a default distribution for a portfolio of homogeneous obligors where individual default drivers are normally distributed. The joint distribution is generated by the use of a t-copula. Returns mean, variance and the quantile chosen.

VaRcredTcop2

Simulates a default distribution for a portfolio of obligors where the individual default driver is normally distributed. The dependence structure imposed corresponds to two homogeneous subportfolios driven by two default factors linked by a t-copula. Returns mean, variance and quantile chosen.

VaRcumulantDG

computes the n-th cumulant for the class of quadratic forms of Gaussian vectors.

VaRcumulantsDG

compute the first n cumulants for the class of quadratic forms of Gaussian vectors.

VaRDGdecomp

uses a generalized eigenvalue decomposition to do a suitable coordinate change. The new risk factors are independently standard normal distributed and the new Hessian matrix (Gamma) is diagonal.

VaRDGdecompG

computes the first and second derivatives with respect to the new risk factors.

VaRdiagplot

produces calibration and discrimination plots which verify the validity of a probability forecasts.

VaRdiagtable

produces table containing frequencies of predictive probabilities of the observations falling into specified intervals.

VaRest

estimates the value at risk (VaR).

VaRestMC

Partial Monte-Carlo method to calculate the Value at Risk (VaR) based on Delta-Gamma Approximation.

VaRestMCcopula

estimates VaR for a given portfolio using copulas

varex

an extended form of the var function - NaN and all values contained in excl are excluded from computation

VaRfitcopula

fits the copula to a given data

VaRgrdiag

produces calibration and discrimination plots which verify validity of probability forecasts.

varimax

performs a varimax rotation of loadings by maximizing the so-called varimax criterion

varimaxval

auxiliary quantlet for varimax, it calculates the value of the varimax negative criterion

VaRmain

sets defaults for library VaR.

varml

computes the maximum likelihood estimates of the model parameters (beta) and covariance (s) of residuals of a VAR(p) model without intercept

VaRopt

defines a list with optional parameters in VaR functions. The list is either created or new options are appended to an existing list.

varorder

standard selection criteria for Full VAR models

VaRpred

predicts the value at risk (VaR).

VaRqDG

computes the a-quantile for the class of quadratic forms of Gaussian vectors; uses Fourier inversion to approximate the cumulative distribution function (CDF).

VaRqqplot

visualizes the reliability of VaR forecasts.

VaRRatMigCount

Derives the matrix of migration counts from the matrix of migration events

VaRRatMigRate

computes the migration rates and the related estimated standard errors from the matrix of migration counts

VaRRatMigRateM

computes the m-period transition rates. Standard deviations of the transition rates are estimated by bootstrap.

VaRsimcopula

generates 2-dimensional random data from distribution with given copula

VaRtest

VaRtest tests all quantlets of the VaR library

VaRtimeplot

shows the time plot of VaR forecasts and the associated changes of the P&L of the portfolio.

varunls

computes the unconstrained least squares estimates of the model parameters (B), residuals (u), variance-covariance matrix of the residuals (s), and autocovariance matrix of the time series (g) of a K-dimensional VAR(p) model with/ without intercept

VaRver

verifies probability forecasts

vec

vec reshapes all given arguments into a vectors and concatenates them into a single vector which is returned.

vec2mat

stores the values of a vector into the upper triangle of a symmetric matrix regarding the sequence described in agglom

volatility

calculates the implied volatility of given options.

volatilityaux

auxiliary quantlet for volatility

volsurf

volsurf computes the implied volatility surface using a Kernel smoothing procedure. Either a Nadaraya-Watson estimator or a local polynomial regression is employed. Both are computed with a quartic Kernel. The metric is either moneyness, i.e. strike devided by the (implied) forward price of the und

volsurfEBBS

computes the implied volatility surface using a local polynomial estimation with an automatic bandwidth selection algorithm. The metric is either moneyness, i.e. strike devided by the (implied) forward price of the underlying, or the original strikes.

volsurfplot

produces a graphic visualising the implied volatility surface computed by the quantlet volsurf. The original options are shown as red points.

volumes

auxiliary quantlet for cartsplit, creates a vector of volumes: for each node of the tree "tr", calculates the volume of the rectangle corresponding to the node.